Data Safety Section (DSS) Form

• Data Safety Section (DSS) forms are structured evaluative instruments that document and assess dataset safety, privacy, and risk using regulatory standards and hazard models.
• They utilize quantitative metrics and risk analysis methods such as FMEA and STPA to determine compliance and trigger necessary mitigation measures.
• DSS forms support practical workflows for developers and data stewards by integrating automated validation, traceability matrices, and continuous monitoring for robust data governance.

A Data Safety Section (DSS) form is a structured evaluative instrument for systematically documenting, assessing, and reporting the safety, privacy, and risk properties of a dataset or data-using system, with origins in both regulatory and technical compliance contexts. DSS forms provide both a workflow for implementers (developers, data managers) and an auditable record for oversight, making the construct central across domains such as statistical disclosure, AI dataset governance, and software platform compliance (notably Google Play's app privacy labeling). DSS form design, contents, and validation criteria are shaped by regulatory standards (e.g., ISO/PAS 8800, App Store requirements), formal risk and hazard modeling frameworks (FMEA, STPA, Five Safes), and emerging trends in dataset quality assurance.

1. Structure and Functional Scope of DSS Forms

DSS forms are characterized by modular structure tailored to the regulatory, operational, and technical setting of the dataset or data-driven system. Notable instantiations include:

• Google Play DSS: Delineated into high-level privacy practices (Data Collection, Data Sharing, Security Practices: “Encrypted in Transit”/“Data Deletion”/“Security Audit”), multi-level data categories and types (14 primary categories, each with subtypes), explicit purposes for collection/sharing (7 purposes), and partner declarations. Selection of options controls which subfields are shown, enforcing progressive disclosure logic (Khandelwal et al., 2023, &&&1&&&).
• Statistical Agencies (Five Safes DSS): Organized as “Safe Projects” (purpose/legal), “Safe People” (authorized users), “Safe Settings” (environmental controls), “Safe Data” (minimization, DP, SDC), and “Safe Outputs” (output checking), with cross-mapping to Contextual Integrity privacy parameters (Bailie et al., 7 Oct 2025).
• Autonomous Driving/AI Dataset DSS: Specifies intended use (e.g., ADAS/AV perception), operational design domains (ODDs), AI Data Flywheel lifecycle, formal hazard/risk registers, mitigation and verification flow, versioned traceability, and ISO compliance (Abbaspour et al., 11 Nov 2025).

The primary fields capture data types, usage, risk factors, and compliance status, forming a record that underpins automated and manual assurance.

2. Risk Identification, Analysis, and Quantitative Criteria

DSS forms encode both qualitative and quantitative risk assessment methods, tailored to relevant hazard models and regulatory standards.

• Hazard Tables: Dataset hazards—class imbalance, annotation errors, non-independence, distribution drift, compression artifacts—are elicited using methods such as FMEA, STPA, HAZOP, FTA; each hazard is characterized by probability ($P_h$), severity ($S_h$), detectability ($D_h$), and quantified risk priority number ($RPN_h = P_h \times S_h \times D_h$). Acceptance requires $RPN_h < 100$ (Abbaspour et al., 11 Nov 2025).
• Dataset Safety Metrics: Completeness $C = N_{\mathrm{covered}} / N_{\mathrm{total}}$, Annotation Accuracy $= 1 - N_{\mathrm{errors}} / N_{\mathrm{labels}}$, Class Balance $f_i \geq 0.02$, Edge-case coverage thresholds, and quantitative scenario coverage for all ODD slices.
• Privacy Metrics (statistical agencies): Differential privacy parameter ($\varepsilon$), sensitivity ($\Delta f$), RMS error, and risk simulation outputs are collated, plus legal/ethical and societal trade-off narrative (Bailie et al., 7 Oct 2025).
• Google Play DSS Consistency: Jaccard-based internal inconsistency $$I_{\text{app}} = 1 - |R_{\text{app}} \cap O_{\text{app}}| / |R_{\text{app}} \cup O_{\text{app}}|$$, Under-reporting $$U_{\text{app}} = |O_{\text{app}} \setminus R_{\text{app}}| / |O_{\text{app}}|$$, Over-reporting $$V_{\text{app}} = |R_{\text{app}} \setminus O_{\text{app}}| / |R_{\text{app}}|$$ (Khandelwal et al., 2023).

These metrics enable objective acceptance thresholds, traceable risk scoring, and persistent monitoring of dataset or app safety posture.

3. Developer and Data Steward Workflows

Completion of a DSS involves multi-stage synthesis of data flow, technical, and legal knowledge:

• App Privacy DSS Process: Inventory codebase and SDKs for data collection/sharing, map flows to DSS categories/purposes, specify security practices, declare purposes and third-party partners, submit for review. Developers often rely on Google’s documentation (80.5%), forums, and Play Console tools. Manual categorization dominates (39.0%), with limited tool adoption; a substantial minority (36.6%) report not categorizing at all (Khedkar et al., 28 Jan 2026, Khandelwal et al., 2023).
• AI Dataset DSS: Define task, ODDs, and sensors; specify Flywheel stages; identify hazards; set formal requirements (e.g., annotation accuracy, independence); execute V&V with standard tools (e.g., TensorFlow Data Validation, nuScenes benchmarks); maintain traceability with versioned audit (Abbaspour et al., 11 Nov 2025).
• Responsible Dataset Design: Stage-by-stage progression (ideation, collection, preprocessing, training/evaluation, release) with explicit metrics (e.g., Fleiss’ $\kappa$ for agreement, class imbalance ratio, toxicity rate, risk score) operationalized via QA/QC scripts and recurring re-audits (Chakraborty, 11 Jun 2025).

Challenges include ambiguity in definitions (“ephemeral processing”), third-party SDK opacity, legal/regulatory alignment, UI complexity, and recurring policy shifts (Khandelwal et al., 2023, Khedkar et al., 28 Jan 2026).

4. Verification, Validation, and Continuous Monitoring

DSS forms encode verification (V&V) logic, typically formalized as acceptance tests, confidence intervals, and quality-check workflows.

• Test-case strategies: Equivalence class and boundary-value testing (e.g., image counts), error guessing, and scenario-specific coverage checks, applying strict pass/fail thresholds (Abbaspour et al., 11 Nov 2025).
• Statistical Validation: Confidence intervals for error rates, scenario coverage, automated schema verification with TensorFlow Data Validation, impact and trustworthiness gap scores (Abbaspour et al., 11 Nov 2025).
• Continuous Monitoring: Distributional drift by KL divergence ($D_{KL}$), drift alerting if threshold exceeded (e.g., $D_{KL} > 0.1$), and active learning for sample selection under uncertainty. Periodic re-audit schedules (e.g., quarterly toxicity tests) are mandated for ongoing risk management (Abbaspour et al., 11 Nov 2025, Chakraborty, 11 Jun 2025).

App platform DSS implementations audit for ongoing consistencies, e.g., via network analysis or permission checks, and may trigger review/rejection if disclosure diverges from observed data flows (Khandelwal et al., 2023).

5. Documentation, Traceability, and Regulatory Compliance

Traceability is an explicit property of the DSS, essential both for reproducibility and external audit.

• Traceability Matrices: Mapping from AI safety requirements to dataset requirement, hazard, mitigation, test case, and V&V result, all linked with unique identifiers and managed in versioned systems (e.g., Git+DVC) (Abbaspour et al., 11 Nov 2025).
• Five Safes DSS: Narrative and tabular documentation for legal/project rationale, user roster, security controls, disclosure control methods, differential privacy configuration, and output catalog/approval (Bailie et al., 7 Oct 2025).
• Responsible Dataset Design: Embedding metadata in machine-readable formats (e.g., Croissant‐RAI JSON), complete datasheets (source, consent, QA/QC, red-team, licenses, versions), and full diff logs for releases (Chakraborty, 11 Jun 2025).

ISO/PAS 8800 alignment typically requires “V-model” traceability (requirements↔design↔implementation↔V&V), completeness of metadata, strict versioning, and routine audits (Abbaspour et al., 11 Nov 2025).

6. Best Practices, Recommendations, and Emerging Directions

Across contexts, DSS best practices include completeness, independence, representativeness, and traceability as core principles (Abbaspour et al., 11 Nov 2025). Recommendations consistently call for:

• Enhanced educational and UI resources (e.g., interactive tutorials, in-line tooltips, real-world and multilingual examples) (Khandelwal et al., 2023, Khedkar et al., 28 Jan 2026).
• Official mapping from APIs/SDKs to DSS categories, canonical machine-readable partner data, and standardized metadata from third-party providers.
• Automated tooling: Static analysis, IDE integration, and pre-submission validation for privacy label accuracy (Khedkar et al., 28 Jan 2026).
• Domain-agnostic measures: Bias/tocsicity filtering, red-teaming, and dataset-centric mitigation (augmentation, deliberate rebalancing, automated annotation review) (Chakraborty, 11 Jun 2025, Abbaspour et al., 11 Nov 2025).
• Integration of advanced security and privacy techniques, e.g., differential privacy parameterizations, vision-language-action datasets for multimodal planning, collaborative perception datasets, and accelerated active learning for new operational domains (Abbaspour et al., 11 Nov 2025).

Societal and ethical trade-offs remain intrinsic to DSS context, with qualitative safeguards (e.g., community advisory, IRB oversight) augmenting quantitative risk controls (Bailie et al., 7 Oct 2025). A plausible implication is that DSS frameworks will continue to evolve towards greater automation, transparency, and interoperability, especially as standards and regulatory mandates adapt to new technological and societal risks.